An Improved Histogram-Based Features in Low-Frequency DCT Domain for Face Recognition

Qiu Chen, Koji Kotani, Feifei Lee, and Tadahiro Ohmi

Abstract—Previously, we proposed an efficient algorithm for facial image recognition combined with vector quantization (VQ) histogram and energy histogram in low-frequency DCT domains. The former algorithm is essential for utilizing the phase information of DCT coefficients by applying binary vector quantization (BVQ) on DCT coefficient blocks. The latter algorithm, energy histogram can be considered to add magnitude information of DCT coefficients. These two histograms, which contain both phase and magnitude information of a DCT transformed facial image, are utilized as a very effective personal feature. In this paper, we propose a novel quantization optimization method for energy histogram according to the maximum entropy principle (MEP) as a design criterion. Publicly available AT&T database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using optimized energy histogram by maximization of information-theoretic entropy can achieve much higher recognition rate.

Qiu Chen is with the Department of Information and Communication Engineering, Kogakuin University, Japan (e-mail: chen@cc.kogakuin.ac.jp).
Koji Kotani is with the Department of Electronics, Graduate School of Engineering, Tohoku University, Japan.
Feifei Lee is with the New Industry Creation Hatchery Center, Tohoku University, Japan, as well as with the University of Shanghai for Science and Technology, China.
Tadahiro Ohmi is with the New Industry Creation Hatchery Center, Tohoku University, Japan.